---
title: "Who cites Zhiping Xiao — 207 papers of our library"
description: "207 papers of the Elamur Research library cite the works of Zhiping Xiao, found in their bibliographies; the most cited first."
canonical: "https://research.elamur.ai/free/en/authors/zhiping-xiao/cited-by/"
---
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# Who cites Zhiping Xiao

211 citations of their works from 207 papers of our library (of the 149,439 whose bibliographies we read) · 52 with a page here, the most cited first · [← Zhiping Xiao](https://md.research.elamur.ai/free/en/authors/zhiping-xiao/)

1. [**DeepSeek-V3.2: Pushing the Frontier of Open Large Language Models arXiv:2512.02556**DeepSeek-AI, Aixin Liu, Aoxue Mei, et al.*2025 · 341 citations*](https://md.research.elamur.ai/free/en/papers/deepseek-ai-2025-deepseek-v3-2-pushing/)
2. [**Small Language Models are the Future of Agentic AI arXiv:2506.02153**Peter Belcak, Greg Heinrich, Shizhe Diao, et al.*2025 · 92 citations*](https://md.research.elamur.ai/free/en/papers/belcak-2025-small-language-models-future-agentic/)
3. **[Insights into DeepSeek-V3: Scaling Challenges and Reflections on Hardware for AI Architectures](https://md.research.elamur.ai/free/en/papers/zhao-2025-insights-deepseek-v3-scaling/) [Proceedings - International Symposium on Computer Architecture/Proceedings](https://md.research.elamur.ai/free/en/venues/proceedings-international-symposium-on-computer-architecture-proceedings/)**Chenggang Zhao, Chengqi Deng, Chong Ruan, et al.*2025 · 74 citations*
4. [**MiniMax-M1: Scaling Test-Time Compute Efficiently with Lightning Attention arXiv:2506.13585**MiniMax, Aili Chen, Aonian Li, et al.*2025 · 66 citations*](https://md.research.elamur.ai/free/en/papers/minimax-2025-minimax-m1-scaling-test/)
5. [**XAttention: Block Sparse Attention with Antidiagonal Scoring arXiv:2503.16428**Ruyi Xu, Guangxuan Xiao, Haofeng Huang, et al.*2025 · 59 citations*](https://md.research.elamur.ai/free/en/papers/xu-2025-xattention-block-sparse-attention-antidiagonal/)
6. [**A Survey of Efficient Reasoning for Large Reasoning Models: Language, Multimodality, and Beyond arXiv:2503.21614**Xiaoye Qu, Yafu Li, Zhao-Chen Su, et al.*2025 · 55 citations*](https://md.research.elamur.ai/free/en/papers/qu-2025-survey-efficient-reasoning-large-reasoning/)
7. [**Kimi Linear: An Expressive, Efficient Attention Architecture arXiv:2510.26692**Kimi Team, Yu Zhang, Zongyu Lin, et al.*2025 · 51 citations*](https://md.research.elamur.ai/free/en/papers/kimi-team-2025-kimi-linear-expressive-efficient/)
8. [**Skywork-Reward-V2: Scaling Preference Data Curation via Human-AI Synergy arXiv:2507.01352**Chris Yuhao Liu, Liang Zeng, Yuzhen Xiao, et al.*2025 · 48 citations*](https://md.research.elamur.ai/free/en/papers/liu-2025-skywork-reward-v2-scaling/)
9. [**Efficient Reasoning Models: A Survey arXiv:2504.10903**Sicheng Feng, Gongfan Fang, Xinyin Ma, et al.*2025 · 37 citations*](https://md.research.elamur.ai/free/en/papers/feng-2025-efficient-reasoning-models-survey/)
10. [**Step-3 is Large yet Affordable: Model-system Co-design for Cost-effective Decoding arXiv:2507.19427**StepFun, Bin Wang, Bojun Wang, et al.*2025 · 26 citations*](https://md.research.elamur.ai/free/en/papers/stepfun-2025-step-3-large-yet/)
11. [**MiniCPM4: Ultra-Efficient LLMs on End Devices arXiv:2506.07900**MiniCPM Team, Chaojun Xiao, Yuxuan Li, et al.*2025 · 22 citations*](https://md.research.elamur.ai/free/en/papers/minicpm-team-2025-minicpm4-ultra-efficient-llms/)
12. **[FinMME: Benchmark Dataset for Financial Multi-Modal Reasoning Evaluation](https://md.research.elamur.ai/free/en/papers/luo-2025-finmme-benchmark-dataset-financial-multi/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Junyu Luo, Zhizhuo Kou, Liming Yang, et al.*2025 · 19 citations*
13. [**Towards Agentic RAG with Deep Reasoning: A Survey of RAG-Reasoning Systems in LLMs arXiv:2507.09477**Yangning Li, Weizhi Zhang, Yuyao Yang, et al.*2025 · 17 citations*](https://md.research.elamur.ai/free/en/papers/li-2025-towards-agentic-rag-deep-reasoning/)
14. **[A Survey on Efficient Large Language Model Training: From Data-centric Perspectives](https://md.research.elamur.ai/free/en/papers/luo-2025-survey-efficient-large-language-model/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Junyu Luo, Bohan Wu, Xiao Luo, et al.*2025 · 14 citations*
15. **[LLM-Based Human-Agent Collaboration and Interaction Systems: A Survey](https://md.research.elamur.ai/free/en/papers/zou-2026-llm-based-human-agent-collaboration/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Henry Peng Zou, Wei-Chieh Huang, Yaozu Wu, et al.*2026 · 12 citations*
16. **[The Sparse Frontier: Sparse Attention Trade-offs in Transformer LLMs](https://md.research.elamur.ai/free/en/papers/nawrot-2026-sparse-frontier-sparse-attention-trade/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Piotr Nawrot, Jianing Li, Renjie Huang, et al.*2026 · 10 citations*
17. **[ReCreate: Reasoning and Creating Domain Agents Driven by Experience](https://md.research.elamur.ai/free/en/papers/hao-2026-recreate-reasoning-creating-domain-agents/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhezheng Hao, Hong Wang, Jian Luo, et al.*2026 · 8 citations*
18. **[EnvScaler: Scaling Tool-Interactive Environments for LLM Agent via Programmatic Synthesis](https://md.research.elamur.ai/free/en/papers/song-2026-envscaler-scaling-tool-interactive-environments/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xiaoshuai Song, Haofei Chang, Guanting Dong, et al.*2026 · 5 citations*
19. **[MultiFinBen: Benchmarking Large Language Models for Multilingual and Multimodal Financial Application](https://md.research.elamur.ai/free/en/papers/peng-2026-multifinben-benchmarking-large-language-models/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xueqing Peng, Lingfei Qian, Yan Wang, et al.*2026 · 4 citations*
20. **[Beyond Dialogue Time: Temporal Semantic Memory for Personalized LLM Agents](https://md.research.elamur.ai/free/en/papers/su-2026-beyond-dialogue-time-temporal-semantic/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Miao Su, Yucan Guo, Zhongni Hou, et al.*2026 · 2 citations*
21. **[Focus-dLLM: Accelerating Long-Context Diffusion LLM Inference via Confidence-Guided Context Focusing](https://md.research.elamur.ai/free/en/papers/long-2026-focus-dllm-accelerating-long-context/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Lingkun Long, Yushi Huang, Shihao Bai, et al.*2026 · 2 citations*
22. **[LoPT: Lossless Parallel Tokenization Acceleration for Long Context Inference of Large Language Model](https://md.research.elamur.ai/free/en/papers/shao-2026-lopt-lossless-parallel-tokenization-acceleration/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Wei Shao, Zheng Lingchao, Pengyu Wang, et al.*2026 · 2 citations*
23. [**MiniCPM-SALA: Hybridizing Sparse and Linear Attention for Efficient Long-Context Modeling arXiv:2602.11761**MiniCPM Team, Wenhao An, Yingfa Chen, et al.*2026 · 2 citations*](https://md.research.elamur.ai/free/en/papers/minicpm-team-2026-minicpm-sala-hybridizing-sparse/)
24. [**MiniMax Sparse Attention arXiv:2606.13392**Xunhao Lai, Weiqi Xu, Yufeng Yang, et al.*2026 · 2 citations*](https://md.research.elamur.ai/free/en/papers/lai-2026-minimax-sparse-attention/)
25. **[CoAct: Co-Active LLM Preference Learning with Human-AI Synergy](https://md.research.elamur.ai/free/en/papers/xu-2026-coact-co-active-llm-preference/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ruiyao Xu, Mihir Parmar, Tiankai Yang, et al.*2026 · 1 citation*
26. **[DELTA: Dynamic Layer-Aware Token Attention for Efficient Long-Context Reasoning](https://md.research.elamur.ai/free/en/papers/zarch-2026-delta-dynamic-layer-aware-token/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Hossein Entezari Zarch, Lei Gao, Chaoyi Jiang, et al.*2026 · 1 citation*
27. **[Easy Samples Are All You Need: Self-Evolving LLMs via Data-Efficient Reinforcement Learning](https://md.research.elamur.ai/free/en/papers/yu-2026-easy-samples-are-all-you/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zhiyin Yu, Bo Zhang, Qibin Hou, et al.*2026 · 1 citation*
28. **[FinCall-Surprise: A Large Scale Multi-modal Benchmark for Earning Surprise Prediction](https://md.research.elamur.ai/free/en/papers/shu-2026-fincall-surprise-large-scale-multi/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Dong Shu, Yanguang Liu, Huopu Zhang, et al.*2026 · 1 citation*
29. **[Rectified Sparse Attention for Efficient Long-Sequence Generation](https://md.research.elamur.ai/free/en/papers/sun-2026-rectified-sparse-attention-efficient-long/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yutao Sun, Tianzhu Ye, Li Dong, et al.*2026 · 1 citation*
30. **[Understanding Structured Financial Data with LLMs: A Case Study on Fraud Detection](https://md.research.elamur.ai/free/en/papers/tan-2026-understanding-structured-financial-data-llms/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xuwei Tan, Yao Ma, Xueru Zhang*2026 · 1 citation*
31. **[What Factors Affect LLMs and RLLMs in Financial Question Answering?](https://md.research.elamur.ai/free/en/papers/wang-2026-what-factors-affect-llms-rllms/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Peng Wang, Xuesi Hu, Jiageng Wu, et al.*2026 · 1 citation*
32. **[When Models Outthink Their Safety: Unveiling and Mitigating Self-Jailbreak in Large Reasoning Models](https://md.research.elamur.ai/free/en/papers/mao-2026-when-models-outthink-their-safety/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yingzhi Mao, Chunkang Zhang, Junxiang Wang, et al.*2026 · 1 citation*
33. **[A Data-Efficient Path to Multilingual LLMs: Language Expansion via Post-training PARAMΔ Integration into Upcycled MoE](https://md.research.elamur.ai/free/en/papers/zhou-2026-data-efficient-path-multilingual-llms/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Hao Zhou, Tianhao Li, Zhijun Wang, et al.*2026*
34. **[A Survey of Reinforcement Learning for Large Language Models under Data Scarcity: Challenges and Solutions](https://md.research.elamur.ai/free/en/papers/yu-2026-survey-reinforcement-learning-large-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhiyin Yu, Yuchen Mou, Juncheng Yan, et al.*2026*
35. **[Beyond Static Synthetic Noise: Assessing the Robustness of Large Language Models to Natural Context Variation in the Real World](https://md.research.elamur.ai/free/en/papers/wu-2026-beyond-static-synthetic-noise-assessing/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yulong Wu, Viktor Schlegel, Riza Batista-Navarro*2026*
36. **[Calibrating Inference Time Alignment with Sequence-level Risk Accumulation](https://md.research.elamur.ai/free/en/papers/tan-2026-calibrating-inference-time-alignment-sequence/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Shanwen Tan, Ziyang Dong, Wei Ju, et al.*2026*
37. **[CITE: Benchmarking Heterogeneous Text-Attributed Graph Models](https://md.research.elamur.ai/free/en/papers/zhang-2026-cite-benchmarking-heterogeneous-text-attributed/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Chenghao Zhang, Qingqing Long, Ludi Wang, et al.*2026*
38. **[DANCE: Diversity-attended Dynamic Caching with Asymmetric Quantization for Test-time Adaptation of Vision-Language Models](https://md.research.elamur.ai/free/en/papers/zou-2026-dance-diversity-attended-dynamic-caching/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Shunge Zou, Changhu Wang, Wei Ju, et al.*2026*
39. **[DataArc-SynData-Toolkit: A Unified Closed-Loop Framework for Multi-Path, Multimodal, and Multilingual Data Synthesis](https://md.research.elamur.ai/free/en/papers/shi-2026-dataarc-syndata-toolkit-unified-closed/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhichao Shi, Cehao Yang, Hao Zhou, et al.*2026*
40. **[Efficient Agent Evaluation via Diversity-Guided User Simulation](https://md.research.elamur.ai/free/en/papers/nakash-2026-efficient-agent-evaluation-diversity-guided/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Itay Nakash, George Kour, Ateret Anaby Tavor*2026*
41. **[Fico: Evaluating Vision-Language Models under Visual Fidelity and Compression at Scale](https://md.research.elamur.ai/free/en/papers/tu-2026-fico-evaluating-vision-language-models/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jianhong Tu, Nicholas Crispino, Kyle Montgomery, et al.*2026*
42. **[Fine-Mem: Fine-Grained Feedback Alignment for Long-Horizon Memory Management](https://md.research.elamur.ai/free/en/papers/ma-2026-fine-mem-fine-grained-feedback/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Weitao Ma, Xiaocheng Feng, Lei Huang, et al.*2026*
43. **[FinMRAGBench: A Realistic and Complex Benchmark for Multi-Modal RAG in Financial Document Analysis](https://md.research.elamur.ai/free/en/papers/yang-2026-finmragbench-realistic-complex-benchmark-multi/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Shouqing Yang, Qi Zhang, Yuhang Yang, et al.*2026*
44. **[Less is More: Knowledge-Aware Compression for Long Legal Judgment Prediction](https://md.research.elamur.ai/free/en/papers/lou-2026-less-is-more-knowledge-aware/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Fanghao Lou, Qiqi Wang, Guanyu Chen, et al.*2026*
45. **[RoBSA: RoPE-based Blockwise Sparse Multi-head Latent Attention](https://md.research.elamur.ai/free/en/papers/shi-2026-robsa-rope-based-blockwise-sparse/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xinyu Shi, Kairong Luo, Zhen Zheng, et al.*2026*
46. **[RRAtention: Dynamic Block Sparse Attention via Per-Head Round-Robin Shifts for Long-Context Inference](https://md.research.elamur.ai/free/en/papers/liu-2026-rratention-dynamic-block-sparse-attention/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Siran Liu, Guoxia Wang, Sa Wang, et al.*2026*
47. **[SciPedia: Unlocking the Value of Scientific Data for Pre-training](https://md.research.elamur.ai/free/en/papers/qin-2026-scipedia-unlocking-value-scientific-data/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yiwei Qin, Zhen Huang, Tiantian Mi, et al.*2026*
48. **[SCOPE: Boosting LLM Efficiency with Scoped Position Encoding](https://md.research.elamur.ai/free/en/papers/qi-2026-scope-boosting-llm-efficiency-scoped/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Qingguo Qi, Hongyang Chen, Zhao Li*2026*
49. **[Text2Mem: A Unified Memory Operation Language for Memory Operating System](https://md.research.elamur.ai/free/en/papers/wang-2026-text2mem-unified-memory-operation-language/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Leo Wang, Lihai Yang, Boyu Chen, et al.*2026*
50. **[The Bitter Lesson of Diffusion Language Models for Agentic Workflows: A Comprehensive Reality Check](https://md.research.elamur.ai/free/en/papers/lu-2026-bitter-lesson-diffusion-language-models/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Qingyu Lu, Liang Ding, Kanjian Zhang, et al.*2026*
51. **[Towards Unified Multimodal Large Language Models: A survey](https://md.research.elamur.ai/free/en/papers/ma-2026-unified-multimodal-large-language-models/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xu Ma, Yitian Zhang, Yun Fu*2026*
52. **[VecInfer: Efficient LLM Inference with Low-Bit KV Cache via Outlier-Suppressed Vector Quantization](https://md.research.elamur.ai/free/en/papers/yao-2026-vecinfer-efficient-llm-inference-low/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Dingyu Yao, Chenxu Yang, Zhengyang Tong, et al.*2026*

155 more citing papers of our library have no page here yet.

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